We generate PLM Ws in both phases: training \(W_{train}\) and testing \(W_{test}\) separately in real-time while executing each step. We are inspired by the idea of the semantic auto-encoder [1]}, [2]}, [3]} in which researchers applied the framework of an auto-encoder to generate a projection function (W) to map input vectors to a latent space. To generate the W, we use only the encoder part of the semantic auto-encoder to generate a project function for each model. The encoder uses the Sylvester equation to find W for each input 3D model. Figure REF  presents a conceptual view of the projection learned model generation where the Sylvester equation takes input data(X,S and\(\lambda \) ) to generate projection learned models (Ws) for both train and test.
Thus, the output of the function is Ws with associated labels. Then, we calculate an average weight (W') of each category to present distinct distributions of categories.
It is obvious that Meta-SeL generates complete separate Ws that enables us to predict test 3D models accurately and precisely. We have three dimensions that are shown in figure REF . First, On the x-axis we have the 3D model categories. Second, we have associated average weights (W's), which are flattened into 1*9 on z-axis. Finally, on Y-axis we have scalar values for the
W's.
